Pricing path-dependent exotic options with flow-based generative networks. - Hyun-Gyoon Kim; Se-Jin Kwon; Jeong-Hoon Kim; Jeonggyu Huh (Appl. Soft Comput., 2022)

Large-scale online learning of implied volatilities. - Tae-Kyoung Kim; Hyun-Gyoon Kim; Jeonggyu Huh (Expert Syst. Appl., 2022)

Forecasting the elasticity of variance with LSTM recurrent neural networks. - Hyun-Gyoon Kim; Jeong-Hoon Kim (Int. J. Comput. Math., 2023)

A stochastic-local volatility model with Le'vy jumps for pricing derivatives. - Hyun-Gyoon Kim; Jeong-Hoon Kim (Appl. Math. Comput., 2023)

A martingale method for option pricing under a CEV-based fast-varying fractional stochastic volatility model. - Hyun-Gyoon Kim; So-Yoon Cho; Jeong-Hoon Kim (Comput. Appl. Math., 2023)

Denoising Task Difficulty-based Curriculum for Training Diffusion Models. - Jin-Young Kim; Hyojun Go; Soonwoo Kwon; Hyun-Gyoon Kim (ICLR, 2025)

Forecasting VIX using interpretable Kolmogorov-Arnold networks. - So-Yoon Cho; Sungchul Lee; Hyun-Gyoon Kim (Expert Syst. Appl., 2025)

Deep learning of optimal exercise boundaries for American options. - Hyun-Gyoon Kim; Jeonggyu Huh (Int. J. Comput. Math., 2025)

ScoreCL: augmentation-adaptive contrastive learning via score-matching function. - Jin-Young Kim; Soonwoo Kwon; Hyojun Go; Yunsung Lee; Seungtaek Choi; Hyun-Gyoon Kim (Mach. Learn., 2025)